Owl and Lizard: Patterns of Head Pose and Eye Pose in Driver Gaze Classification

Accurate, robust, inexpensive gaze tracking in the car can help keep a driver safe by facilitating the more effective study of how to improve (1) vehicle interfaces and (2) the design of future Advanced Driver Assistance Systems. In this paper, we estimate head pose and eye pose from monocular video using methods developed extensively in prior work and ask two new interesting questions. First, how much better can we classify driver gaze using head and eye pose versus just using head pose? Second, are there individual-specific gaze strategies that strongly correlate with how much gaze classification improves with the addition of eye pose information? We answer these questions by evaluating data drawn from an on-road study of 40 drivers. The main insight of the paper is conveyed through the analogy of an "owl" and "lizard" which describes the degree to which the eyes and the head move when shifting gaze. When the head moves a lot ("owl"), not much classification improvement is attained by estimating eye pose on top of head pose. On the other hand, when the head stays still and only the eyes move ("lizard"), classification accuracy increases significantly from adding in eye pose. We characterize how that accuracy varies between people, gaze strategies, and gaze regions.Comment: Accepted for Publication in IET Computer Vision. arXiv admin note: text overlap with arXiv:1507.0476

Can user-paced, menu-free spoken language interfaces improve dual task handling while driving?

The use of speech-based interaction over traditional means of interaction in secondary tasks may increase safety in demanding environments with high requirements on operator attention. Speech interfaces have suffered from issues similar to those of visual displays, as they often rely on a complex menu structure that corresponds to that of visual systems. Recent advances in speech technology allow the use of natural language, eliminating the need for menu structures and offering a tighter coupling between the intention to act and the completion of the action. Modern speech technology may not only make already existing types of interaction safer, but also opens up for new applications, which may enhance safety. One such application is a speech-based hazard reporting system. A small fixed-base simulator study showed that drivers adapt the timing of the hazard reports to the situation at hand, such that an increase in reported workload was avoided